Improving classification of epileptic and non-epileptic EEG events by feature selection

This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.Peer reviewe

A Multi-Tier Distributed fog-based Architecture for Early Prediction of Epileptic Seizures

Epilepsy is the fourth most common neurological problem. With 50 million people living with epilepsy worldwide, about one in 26 people will continue experiencing recurring seizures during their lifetime. Epileptic seizures are characterized by uncontrollable movements and can cause loss of awareness. Despite the optimal use of antiepileptic medications, seizures are still difficult to control due to their sudden and unpredictable nature. Such seizures can put the lives of patients and others at risk. For example, seizure attacks while patients are driving could affect their ability to control a vehicle and could result in injuries to the patients as well as others. Notifying patients before the onset of seizures can enable them to avoid risks and minimize accidents, thus, save their lives. Early and accurate prediction of seizures can play a significant role in improving patients’ quality of life and helping doctors to administer medications through providing a historical overview of patient's condition over time. The individual variability and the dynamic disparity in differentiating between the pre-ictal phase (a period before the onset of the seizure) and other seizures phases make the early prediction of seizures a challenging task. Although several research projects have focused on developing a reliable seizure prediction model, numerous challenges still exist and need to be addressed. Most of the existing approaches are not suitable for real-time settings, which requires bio-signals collection and analysis in real-time. Various methods were developed based on the analysis of EEG signals without considering the notification latency and computational cost to support monitoring of multiple patients. Limited approaches were designed based on the analysis of ECG signals. ECG signals can be collected using consumer wearable devices and are suitable for light-weight real-time analysis. Moreover, existing prediction methods were developed based on the analysis of seizure state and ignored the investigation of pre-ictal state. The analysis of the pre-ictal state is essential in the prediction of seizures at an early stage. Therefore, there is a crucial need to design a novel computing model for early prediction of epileptic seizures. This model would greatly assist in improving the patients' quality of lives. This work proposes a multi-tier architecture for early prediction of seizures based on the analysis of two vital signs, namely, Electrocardiography (ECG) and Electroencephalogram (EEG) signals. The proposed architecture comprises of three tiers: (1) sensing at the first tier, (2) lightweight analysis based on ECG signals at the second tier, and (3) deep analysis based on EEG signals at the third tier. The proposed architecture is developed to leverage the potential of fog computing technology at the second tier for a real-time signal analytics and ubiquitous response. The proposed architecture can enable the early prediction of epileptic seizures, reduce the notification latency, and minimize the energy consumption on real-time data transmissions. Moreover, the proposed architecture is designed to allow for both lightweight and extensive analytics, thus make accurate and reliable decisions. The proposed lightweight model is formulated using the analysis of ECG signals to detect the pre-ictal state. The lightweight model utilizes the Least Squares Support Vector Machines (LS-SVM) classifier, while the proposed extensive analytics model analyzes EEG signals and utilizes Deep Belief Network (DBN) to provide an accurate classification of the patient’s state. The performance of the proposed architecture is evaluated in terms of latency minimization and energy consumption in comparison with the cloud. Moreover, the performance of the proposed prediction models is evaluated using three datasets. Various performance metrics were used to investigate the prediction model performance, including: accuracy, sensitivity, specificity, and F1-Measure. The results illustrate the merits of the proposed architecture and show significant improvement in the early prediction of seizures in terms of accuracy, sensitivity, and specificity

An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection

This research aims to develop a data-driven computationally efficient strategy for automatic cross-patient seizure detection using spatio temporal features learned from multichannel electroencephalogram (EEG) time-series data. In this approach, we utilize an algorithm that seeks to capture spectral, temporal, and spatial information in order to achieve high generalization. This algorithm's initial step is to convert EEG signals into a series of temporal and multi-spectral pictures. The produced images are then sent into a convolutional neural network (CNN) as inputs. Our convolutional neural network as a deep learning method learns a general spatially irreducible representation of a seizure to improves sensitivity, specificity, and accuracy results comparable to the state-of-the-art results. In this work, in order to avoid the inherent high computational cost of CNNs while benefiting from their superior classification performance, a neuromorphic computing strategy for seizure prediction called spiking CNN is developed from the traditional CNN method, which is motivated by the energy-efficient spiking neural networks (SNNs) of the human brain

Classificação de sinais eletroencefalográficos utilizando Transformada Wavelet Discreta e Máquina de Vetores de Suporte: uma aplicação na diferenciação entre crises epilépticas e crises não epilépticas psicogênicas

O presente trabalho aborda o estudo e aplicação da Transformada Wavelet Discreta (DWT) em conjunto com o classificador do tipo Máquina de Vetores de Suporte (SVM) na diferenciação entre crises epilépticas e crises não epilépticas psicogênicas (CNEP). Um banco de dados com exames de eletroencefalograma (EEG) contendo crises epilépticas e crises não epilépticas psicogênicas foi coletado na Unidade de Videoeletroencefalografia do Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (IPq-HCFMUSP). No processamento do sinal EEG, foi utilizada a Transformada Wavelet Discreta (DWT) baseada nas famílias Coiflet 1 e Daubechies 4 e a extração direta do sinal (sem usar DWT). A partir desses processamentos, foram gerados vetores de características para o treinamento e avaliação do classificador SVM. Na análise do desempenho do classificador, foram realizados testes modificando-se o número de vetores de características para o treinamento do classificador, a origem do vetor de características (Coiflet 1, Daubechies 4 e extração direta) e o tipo de kernel (Linear, Polinomial, Função de Base Radial - RBF - e Sigmoide). Como resultado, no caso do emprego de janelas de 1 segundo no processamento do sinal EEG, o classificador foi capaz de atingir uma taxa de acerto (acurácia) de até 100% usando o kernel Linear e as famílias Coiflet 1 e Daubechies 4. No caso da utilização do tempo total de cada crise, o classificador obteve uma taxa de acerto de até 100% nos quatro tipo de kernel usando a família Coiflet 1. Desse modo, com base nos vetores de características utilizados, foi possível concluir que o classificador SVM é eficiente e o seu uso é viável na diferenciação entre crise epiléptica e CNEP.The present work deals with the study and application of the Discrete Wavelet Transform (DWT) in conjunction with the Supporting Vector Machine (SVM) classifier in the differentiation between epileptic seizures and psychogenic non-epileptic seizures (PNES). A database with electroencephalogram (EEG) tests containing epileptic seizures and psychogenic non-epileptic seizures was collected at the Videoelectroencephalography Unit of the Institute of Psychiatry of the Hospital das Clínicas of the Medical School of the University of São Paulo (IPq-HCFMUSP). In the EEG signal processing, the Wavelet Discrete Transform (DWT) based on the Coiflet 1 and Daubechies 4 families and the direct signal extraction (without DWT) were used. From these processing, characteristic vectors were generated for the training and evaluation of the SVM classifier. In the analysis of the performance of the classifier, tests were performed by modifying the number of characteristics vectors for the classifier training, the origin of the characteristic vector (Coiflet 1, Daubechies 4 and direct extraction) and the kernel type (Linear, Polynomial , Radial Base Function - RBF - and Sigmoid). As a result, in the case of the use of 1-second windows in the EEG signal processing, the classifier was able to achieve a hit rate (accuracy) of up to 100% using the Linear kernel and the Coiflet 1 and Daubechies 4 families. In the case of the use of the total time of each crisis, the classifier obtained a hit rate of up to 100% in the four kernel types using the Coiflet 1 family. Thus, based on the feature vectors used, it was possible to conclude that the classifier SVM is efficient and its use is feasible in the differentiation between epileptic seizures and PNES